code: def test_parallel_post_fit_transform(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
      )
      base = PCA(random_state=0)
      wrap = ParallelPostFit(PCA(random_state=0))
  
      base.fit(raw_x, raw_y)
      wrap.fit(X, y)
  
      result = base.transform(X)
      expected = wrap.transform(X)
      np.testing.assert_allclose(result, expected, atol=0.1)
  
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 3 for calling a global function with example usage: test_parallel_post_fit_transform(setup) without return types