code: def _backtracked_lcs(
      lcs_table: Sequence[Sequence[int]],
      pred_tokens: Sequence[str],
      target_tokens: Sequence[str],
  ) -> Sequence[int]:
      """Backtrack LCS table.
  
      Args:
          lcs_table: A table containing information for the calculation of the longest common subsequence.
          pred_tokens: A tokenized predicted sentence.
          target_tokens: A tokenized target sentence.
  
      """
      i = len(pred_tokens)
      j = len(target_tokens)
      backtracked_lcs: List[int] = []
      while i > 0 and j > 0:
          if pred_tokens[i - 1] == target_tokens[j - 1]:
              backtracked_lcs.insert(0, j - 1)
              i -= 1
              j -= 1
          elif lcs_table[j][i - 1] > lcs_table[j - 1][i]:
              i -= 1
          else:
              j -= 1
      return backtracked_lcs
  
file_path: torchmetrics/src/torchmetrics/functional/text/rouge.py
imports: import re
  from collections import Counter
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor, tensor
  from typing_extensions import Literal
  from torchmetrics.utilities.imports import _NLTK_AVAILABLE
label_desc: Use image node_id 5 for calling a global function with example usage: _backtracked_lcs(lcs_table, pred_tokens, target_tokens) and returns: backtracked_lcs