code: def _check_multimetric_scoring(estimator, scoring=None):
      # TODO: See if scikit-learn 0.24 solves the need for using
      # a private method
      from sklearn.metrics import check_scoring
      from sklearn.metrics._scorer import _check_multimetric_scoring
  
      if callable(scoring) or isinstance(scoring, (type(None), str)):
          scorers = {"score": check_scoring(estimator, scoring=scoring)}
          return scorers, False
      return _check_multimetric_scoring(estimator, scoring), True
  
file_path: dask-ml/dask_ml/_compat.py
imports: import contextlib
  import os
  from collections.abc import Mapping
  from typing import Any
  import dask
  import dask.array
  import distributed
  import packaging.version
  import pandas
  import sklearn
  import sklearn.utils.validation
label_desc: Use image node_id 2 for calling a global function with example usage: _check_multimetric_scoring(estimator, scoring) and returns: _check_multimetric_scoring,  True, scorers,  False