code: def test_log_loss_scoring(y):
      # a_scorer = sklearn.metrics.get_scorer('neg_log_loss')
      # b_scorer = dask_ml.metrics.get_scorer('neg_log_loss')
      X = da.random.uniform(size=(4, 2), chunks=2)
      labels = np.unique(y)
      y = da.from_array(np.array(y), chunks=2)
  
      a_scorer = sklearn.metrics.make_scorer(
          sklearn.metrics.log_loss,
          greater_is_better=False,
          needs_proba=True,
          labels=labels,
      )
      b_scorer = sklearn.metrics.make_scorer(
          dask_ml.metrics.log_loss,
          greater_is_better=False,
          needs_proba=True,
          labels=labels,
      )
  
      clf = dask_ml.wrappers.ParallelPostFit(
          sklearn.linear_model.LogisticRegression(
              n_jobs=1, solver="lbfgs", multi_class="auto"
          )
      )
      clf.fit(*dask.compute(X, y))
  
      result = b_scorer(clf, X, y)
      expected = a_scorer(clf, *dask.compute(X, y))
  
      assert_eq(result, expected)
  
file_path: dask-ml/tests/metrics/test_metrics.py
imports: import dask
  import dask.array
  import numpy
  import numpy.testing
  import pytest
  import sklearn
  import sklearn.linear_model
  import sklearn.metrics
  from dask.array.utils import assert_eq
  import dask_ml.metrics
  import dask_ml.wrappers
label_desc: Use image node_id 8 for calling a global function with example usage: test_log_loss_scoring(y) without return types