code: def __init__(
      self,
      randomize_ties: bool = True,
      min_num_obs_per_arm: int = 1,
      *,
      n_arms: Optional[int] = None,
      arm_ids: Optional[List[str]] = None,
  ) -> None:
      super().__init__()
      if n_arms is not None:
          self.arm_ids = list(map(str, range(n_arms)))
          self.n_arms = n_arms
      if arm_ids is not None:
          self.arm_ids = arm_ids
          self.n_arms = len(arm_ids)
      self.min_num_obs_per_arm = min_num_obs_per_arm
      self.total_n_obs_all_arms = 0
      self.total_n_obs_per_arm = torch.zeros(self.n_arms)
      self.total_sum_reward_per_arm = torch.zeros(self.n_arms)
      self.total_sum_reward_squared_per_arm = torch.zeros(self.n_arms)
      self.randomize_ties = randomize_ties
  
imports: from abc import ABC, abstractmethod
  from typing import List, Optional, Tuple
  import torch
  from torch import Tensor
label_desc: Use image node_id 1 to create a new MABAlgo object from inherited base classes: torch.nn, ABC with example: obj = MABAlgo(randomize_ties, min_num_obs_per_arm)