docstr: A utility method used to create the bandit, feed in a batch of observations and get the scores in one function call
  
  Args:
      n_obs_per_arm (Tensor): A tensor of counts of per-arm numbers of observations
      sum_reward_per_arm (Tensor): A tensor of sums of rewards for each arm
      sum_reward_squared_per_arm (Tensor): A tensor of sums of squared rewards for each arm
  
  Returns:
      Tensor: Array of per-arm scores
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code: def get_scores_from_batch(
      cls,
      n_obs_per_arm: Tensor,
      sum_reward_per_arm: Tensor,
      sum_reward_squared_per_arm: Tensor,
  ) -> Tensor:
      """
      A utility method used to create the bandit, feed in a batch of observations and get the scores in one function call
  
      Args:
          n_obs_per_arm (Tensor): A tensor of counts of per-arm numbers of observations
          sum_reward_per_arm (Tensor): A tensor of sums of rewards for each arm
          sum_reward_squared_per_arm (Tensor): A tensor of sums of squared rewards for each arm
  
      Returns:
          Tensor: Array of per-arm scores
      """
      n_arms = len(n_obs_per_arm)
      b = cls(n_arms=n_arms)  # pyre-ignore[45]
      b.add_batch_observations(
          n_obs_per_arm, sum_reward_per_arm, sum_reward_squared_per_arm
      )
      return b()
  
imports: from abc import ABC, abstractmethod
  from typing import List, Optional, Tuple
  import torch
  from torch import Tensor
label_desc: Use image node_id 9 for calling the MABAlgo obj's underlying member method code with example usage: obj.get_scores_from_batch(cls, n_obs_per_arm, sum_reward_per_arm, sum_reward_squared_per_arm) and returns: b