code: def _reorder_tensors_as(tensors, ordered_tensors):
      """Assume that tensors are of same order as ordered_tensors within their
      types, e.g., from _take_tensors. Reorder them to be of same order as
      ordered_tensors.
  
      Args:
          tensors (Iterable[Tensor]): tensors to be reordered. They should be of
            the same order as ordered_tensors within their own types.
          ordered_tensors (Iterable[Tensor]): tensors whose order will be the
            reference.
  
      Returns:
          Ordered tuple of tensors with contents from tensors and order of
          ordered_tensors.
      """
      type_dict = defaultdict(list)
      for tensor in tensors:
          type_dict[tensor.type()].append(tensor)
      type_dict_ = {t: iter(coll) for t, coll in type_dict.items()}
      return tuple(
          next(type_dict_[tensor.type()]) for tensor in ordered_tensors
      )
  
imports: import copyreg
  import functools
  import sys
  import traceback
  import warnings
  from collections import defaultdict
  from typing import Any, DefaultDict, List, Optional
  import torch
label_desc: Use image node_id 26 for calling a global function with example usage: _reorder_tensors_as(tensors, ordered_tensors) and returns: tuple