code: def _compute_sentence_statistics(
      preds_word: str,
      target_words: Union[str, Sequence[str]],
      alpha: float = 2.0,
      rho: float = 0.3,
      deletion: float = 0.2,
      insertion: float = 1.0,
  ) -> Tensor:
      """Compute scores for ExtendedEditDistance.
  
      Args:
          target_words: An iterable of reference words
          preds_word: A hypothesis word
          alpha: An optimal jump penalty, penalty for jumps between characters
          rho: coverage cost, penalty for repetition of characters
          deletion: penalty for deletion of character
          insertion: penalty for insertion or substitution of character
  
      Return:
          best_score: best (lowest) sentence-level score as a Tensor
  
      """
      best_score = inf
  
      for reference in target_words:
          score = _eed_function(
              preds_word, reference, alpha, rho, deletion, insertion
          )
          if score < best_score:
              best_score = score
  
      return tensor(best_score)
  
file_path: torchmetrics/src/torchmetrics/functional/text/eed.py
imports: import re
  import unicodedata
  from math import inf
  from typing import List, Optional, Sequence, Tuple, Union
  from torch import Tensor, stack, tensor
  from typing_extensions import Literal
  from torchmetrics.functional.text.helper import _validate_inputs
label_desc: Use image node_id 7 for calling a global function with example usage: _compute_sentence_statistics(preds_word, target_words, alpha, rho, deletion, insertion) and returns: tensor