code: def reindex_multiple_tensors(
      all_ids: List[str],
      batch_ids: Optional[List[str]],
      value_tensors: Tuple[Tensor, ...],
  ) -> Tuple[Tensor, ...]:
      """
      Each tensor from value_tensors is ordered by ids from batch_ids. In the output we
          return these tensors reindexed by all_ids, filling in zeros for missing entries.
  
      Args:
          all_ids (List[str]): The IDs that specify how to order the elements in the output
          batch_ids (Optional[List[str]]): The IDs that specify how the elements are ordered in the input
          value_tensors (Tuple[Tensor]): A tuple of tensors with elements ordered by `batch_ids`
      Return:
          A Tuple of reindexed tensors
      """
      if batch_ids is None or batch_ids == all_ids:
          # the observations are for all arms are already in correct order
          return value_tensors
      else:
          assert len(batch_ids) == len(
              set(batch_ids)
          )  # make sure no duplicates in arm IDs
  
          # get the indices of the arms
          arm_idxs = get_arm_indices(all_ids, batch_ids)
  
          # put elements from the batch in the positions specified by `arm_ids` (missing arms will be zero)
          ret = []
          for v in value_tensors:
              ret.append(
                  place_values_at_indices(v, arm_idxs, len(all_ids))
              )
          return tuple(ret)
  
imports: from abc import ABC, abstractmethod
  from typing import List, Optional, Tuple
  import torch
  from torch import Tensor
label_desc: Use image node_id 3 for calling a global function with example usage: reindex_multiple_tensors(all_ids, batch_ids, value_tensors) and returns: value_tensors, tuple