code: def test_parallel_post_fit_multiclass(setup):
      raw_x, raw_y = make_classification(n_samples=1000)
      X, y = mt.tensor(raw_x, chunk_size=100), mt.tensor(
          raw_y, chunk_size=100
      )
      raw_x, raw_y = make_classification(n_classes=3, n_informative=4)
      X, y = mt.tensor(raw_x, chunk_size=50), mt.tensor(
          raw_y, chunk_size=50
      )
  
      clf = ParallelPostFit(
          LogisticRegression(
              random_state=0,
              n_jobs=1,
              solver="lbfgs",
              multi_class="auto",
          )
      )
  
      clf.fit(X, y)
      result = clf.predict(X)
      expected = clf.estimator.predict(X)
  
      np.testing.assert_allclose(result, expected)
  
      result = clf.predict_proba(X)
      expected = clf.estimator.predict_proba(X)
  
      np.testing.assert_allclose(result, expected)
  
      result = clf.predict_log_proba(X)
      expected = clf.estimator.predict_log_proba(X)
  
      np.testing.assert_allclose(result, expected)
  
file_path: mars/mars/learn/tests/test_wrappers.py
imports: import numpy
  import pytest
  from sklearn.datasets import make_classification
  from sklearn.decomposition import PCA
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.linear_model import LinearRegression, LogisticRegression
  from ...None import tensor
  from ..wrappers import ParallelPostFit
label_desc: Use image node_id 4 for calling a global function with example usage: test_parallel_post_fit_multiclass(setup) without return types