code: def _rouge_score_compute(
      sentence_results: Dict[str, List[Tensor]]
  ) -> Dict[str, Tensor]:
      """Compute the combined ROUGE metric for all the input set of predicted and target sentences.
  
      Args:
          sentence_results: Rouge-N/Rouge-L/Rouge-LSum metrics calculated for single sentence.
  
      """
      results: Dict[str, Tensor] = {}
      # Obtain mean scores for individual rouge metrics
      if sentence_results == {}:
          return results
  
      for rouge_key, scores in sentence_results.items():
          results[rouge_key] = torch.tensor(scores).mean()
  
      return results
  
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 12 for calling a global function with example usage: _rouge_score_compute(sentence_results) and returns: results, results