code: def _evaluate(
      self,
      input: BanditsEstimatorInput,
      train_samples: Sequence[LogSample],
      eval_samples: Sequence[LogSample],
      force_train: bool = False,
      **kwargs,
  ) -> Optional[EstimatorResult]:
      logger.info("OPE Switch Evaluating")
      self._train_model(train_samples, force_train)
  
      if "exp_base" in kwargs:
          exp_base = kwargs["exp_base"]
      else:
          exp_base = SwitchEstimator.EXP_BASE
      if "candidates" in kwargs:
          num_candidates = kwargs["candidates"]
      else:
          num_candidates = SwitchEstimator.CANDIDATES
  
      (
          actions,
          ws,
          rs,
          r_est,
          r_est_for_logged_action,
          propensities,
          expected_rmax,
          log_avg,
          gt_avg,
      ) = self._calc_weight_reward_tensors(input, eval_samples)
      min_w, max_w = float(torch.min(ws).item()), float(
          torch.max(ws).item()
      )
      diff = max_w - min_w
      # The threshold lies in the range [min ips, max ips]
      # Picking a small threshold -> using mainly the model-based estimator
      # Picking a large threshold -> using mainly the ips-based estimator
      candidates = [
          min_w
          + ((exp_base**x) / (exp_base ** (num_candidates - 1)))
          * diff
          for x in range(num_candidates)
      ]
      # This prevents the edge case where nearly all scores being min_w prevents
      # switch from trying a purely DM estimate
      tau = min_w - SwitchEstimator.EPSILON
      loss = float("inf")
      for candidate in candidates:
          estimated_values = self._calc_estimated_values(
              rs,
              ws,
              actions,
              candidate,
              r_est,
              r_est_for_logged_action,
              propensities,
          )
          var = (1.0 / (estimated_values.shape[0] ** 2)) * torch.sum(
              # pyre-fixme[58]: `**` is not supported for operand types `Tensor`
              #  and `int`.
              (estimated_values - torch.mean(estimated_values))
              ** 2
          ).item()
          bias = torch.mean(
              torch.sum(
                  expected_rmax * (ws > candidate).float(),
                  dim=1,
                  keepdim=True,
              )
          ).item()
          # pyre-fixme[6]: For 1st param expected `int` but got `Union[bool,
          #  float, int]`.
          cand_loss = var + bias * bias
          if cand_loss < loss:
              tau = candidate
              loss = cand_loss
  
      estimated_values = self._calc_estimated_values(
          rs,
          ws,
          actions,
          tau,
          r_est,
          r_est_for_logged_action,
          propensities,
      )
      (
          tgt_score_normalized,
          tgt_std_err,
          tgt_std_err_normalized,
      ) = self._compute_metric_data(
          estimated_values.detach(), log_avg.average
      )
      return EstimatorResult(
          log_reward=log_avg.average,
          estimated_reward=torch.mean(estimated_values).item(),
          ground_truth_reward=gt_avg.average,
          estimated_weight=float(estimated_values.shape[0]),
          estimated_reward_normalized=tgt_score_normalized,
          estimated_reward_std_error=tgt_std_err,
          estimated_reward_normalized_std_error=tgt_std_err_normalized,
      )
  
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 5 for calling the SwitchEstimator obj's underlying member method code with example usage: obj._evaluate(input, train_samples, eval_samples, force_train) and returns: EstimatorResult