class_docstr: A convenience class for calculating the Levenshtein edit distance.
  
  Class will cache some intermediate values to hasten the calculation. The implementation follows the implementation
  from https://github.com/mjpost/sacrebleu/blob/master/sacrebleu/metrics/lib_ter.py,
  where the most of this implementation is adapted and copied from.
  
  Args:
      reference_tokens: list of reference tokens
      op_insert: cost of insertion operation
      op_delete: cost of deletion operation
      op_substitute: cost of substitution operation
class_docstr_tok: ["A","convenience","class","for","calculating","the","Levenshtein","edit","distance",".","Class","will","cache","some","intermediate","values","to","hasten","the","calculation",".","The","implementation","follows","the","implementation","from","https",":","\/\/github.com\/mjpost\/sacrebleu\/blob\/master\/sacrebleu\/metrics\/lib_ter.py",",","where","the","most","of","this","implementation","is","adapted","and","copied","from",".","Args",":","reference_tokens",":","list","of","reference","tokens","op_insert",":","cost","of","insertion","operation","op_delete",":","cost","of","deletion","operation","op_substitute",":","cost","of","substitution","operation"]
docstr: Precomputed empty matrix row for Levenhstein edit distance.
  
  Args:
      length: A length of a tokenized sentence.
  
  Return:
      A list of tuples containing infinite edit operation costs and yet undefined edit operations.
docstr_tok: ["Precomputed","empty","matrix","row","for","Levenhstein","edit","distance",".","Args",":","length",":","A","length","of","a","tokenized","sentence",".","Return",":","A","list","of","tuples","containing","infinite","edit","operation","costs","and","yet","undefined","edit","operations","."]
code: def _get_empty_row(
      self, length: int
  ) -> List[Tuple[int, _EditOperations]]:
      """Precomputed empty matrix row for Levenhstein edit distance.
  
      Args:
          length: A length of a tokenized sentence.
  
      Return:
          A list of tuples containing infinite edit operation costs and yet undefined edit operations.
  
      """
      return [
          (int(self.op_undefined), _EditOperations.OP_UNDEFINED)
      ] * (length + 1)
  
file_path: torchmetrics/src/torchmetrics/functional/text/helper.py
imports: import math
  from enum import Enum, unique
  from typing import Dict, List, Sequence, Tuple, Union
label_desc: Use image node_id 7 for calling the _LevenshteinEditDistance obj's underlying member method code with example usage: obj._get_empty_row(length) and returns: unknown