code: def _rouge_l_score(
      pred: Sequence[str], target: Sequence[str]
  ) -> Dict[str, Tensor]:
      """Compute precision, recall and F1 score for the Rouge-L metric.
  
      Args:
          pred: A predicted sentence.
          target: A target sentence.
  
      """
      pred_len, target_len = len(pred), len(target)
      if 0 in (pred_len, target_len):
          return {
              "precision": tensor(0.0),
              "recall": tensor(0.0),
              "fmeasure": tensor(0.0),
          }
  
      lcs: int = _lcs(pred, target)  # type: ignore
      return _compute_metrics(lcs, pred_len, target_len)
  
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 9 for calling a global function with example usage: _rouge_l_score(pred, target) and returns: _compute_metrics, dict