code: def test_parallel_post_fit_basic(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
      )
      clf = ParallelPostFit(GradientBoostingClassifier())
      clf.fit(X, y)
  
      assert isinstance(clf.predict(X), mt.Tensor)
      assert isinstance(clf.predict_proba(X), mt.Tensor)
  
      result = clf.score(X, y)
      expected = clf.estimator.score(X, y)
      assert result.fetch() == expected
  
      clf = ParallelPostFit(LinearRegression())
      clf.fit(X, y)
      with pytest.raises(
          AttributeError,
          match="The wrapped estimator (.|\n)* 'predict_proba' method.",
      ):
          clf.predict_proba(X)
  
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 1 for calling a global function with example usage: test_parallel_post_fit_basic(setup) without return types