code: def get_bernoulli_ucb_tuned_scores(
      n_obs_per_arm: Tensor, num_success_per_arm: Tensor
  ) -> Tensor:
      """
      a minimalistic function that implements UCB-Tuned for Bernoulli bandit
      it's here only to benchmark execution time penalty incurred by the class-based implementation
      """
      avg_rewards = num_success_per_arm / n_obs_per_arm
      log_t_over_ni = (
          torch.log(torch.sum(n_obs_per_arm)) / n_obs_per_arm
      )
      per_arm_var_est = (
          avg_rewards
          # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
          - avg_rewards**2
          + torch.sqrt(
              2 * log_t_over_ni
          )  # additional term to make the estimate conservative (unlikely to underestimate)
      )
      # pyre-fixme[6]: For 2nd param expected `Tensor` but got `float`.
      return avg_rewards + torch.sqrt(
          log_t_over_ni * torch.fmin(per_arm_var_est, 0.25)
      )
  
imports: import math
  from abc import ABC, abstractmethod
  from typing import List, Optional
  import torch
  from reagent.mab.mab_algorithm import MABAlgo
  from torch import Tensor
label_desc: Use image node_id 1 for calling a global function with example usage: get_bernoulli_ucb_tuned_scores(n_obs_per_arm, num_success_per_arm) and returns: unknown