code: def use_score_func(
      func_name: str, y_true: np.ndarray, y_pred: np.ndarray
  ) -> float:
      """
      Call the given scoring function.
  
      This takes care of handling keyword arguments that were pre-specified
      when creating the scorer. This applies any sign-flipping that was
      specified by ``make_scorer()`` when the scorer was created.
  
      Parameters
      ----------
      func_name : str
          The name of the objective function to use.
      y_true : numpy.ndarray
          The true/actual/gold labels for the data.
      y_pred : numpy.ndarray
          The predicted/observed labels for the data.
  
      Returns
      -------
      float
          The scored result from the given scorer.
      """
      try:
          scorer = get_scorer(func_name)
      except ValueError:
          scorer = _CUSTOM_METRICS[func_name]
  
      return scorer._sign * scorer._score_func(
          y_true, y_pred, **scorer._kwargs
      )
  
file_path: skll/skll/metrics.py
imports: import copy
  import sys
  from importlib import import_module
  from inspect import signature
  from pathlib import Path
  from typing import Optional, Union
  import numpy
  from scipy.stats import kendalltau, pearsonr, spearmanr
  from sklearn.metrics import confusion_matrix, f1_score, fbeta_score, get_scorer, get_scorer_names, make_scorer
  from skll.types import PathOrStr
label_desc: Use image node_id 5 for calling a global function with example usage: use_score_func(func_name, y_true, y_pred) and returns: unknown