code: def _compute(
      self,
      predictions,
      references,
      keep_singletons=True,
      NP_only=False,
      min_span=False,
      remove_nested=False,
  ):
      allmetrics = [
          ("mentions", evaluator.mentions),
          ("muc", evaluator.muc),
          ("bcub", evaluator.b_cubed),
          ("ceafe", evaluator.ceafe),
          ("lea", evaluator.lea),
      ]
  
      if min_span:
          has_gold_parse = util.check_gold_parse_annotation(references)
          if not has_gold_parse:
              raise NotImplementedError(
                  "References should have gold parse annotation to use 'min_span'."
              )
              # util.parse_key_file(key_file)
              # key_file = key_file + ".parsed"
  
      score = compute_score(
          key_lines=references,
          sys_lines=predictions,
          metrics=allmetrics,
          NP_only=NP_only,
          remove_nested=remove_nested,
          keep_singletons=keep_singletons,
          min_span=min_span,
      )
  
      return score
  
file_path: evaluate/metrics/coval/coval.py
imports: import coval
  import datasets
  from coval.conll import reader, util
  from coval.eval import evaluator
  import evaluate
label_desc: Use image node_id 2 for calling the Coval obj's underlying member method code with example usage: obj._compute(predictions, references, keep_singletons, NP_only, min_span, remove_nested) and returns: score