code: def test_whitening(svd_solver):
      # Test that PCA and IncrementalPCA transforms match to sign flip.
      X = datasets.make_low_rank_matrix(
          1000,
          10,
          tail_strength=0.0,
          effective_rank=2,
          random_state=1999,
      )
      X = da.from_array(X, chunks=[200, -1])
      prec = 3
      n_samples, n_features = X.shape
      for nc in [None, 9]:
          pca = PCA(
              whiten=True, n_components=nc, svd_solver=svd_solver
          ).fit(X.compute())
          ipca = IncrementalPCA(
              whiten=True,
              n_components=nc,
              batch_size=250,
              svd_solver=svd_solver,
          ).fit(X)
  
          Xt_pca = pca.transform(X)
          Xt_ipca = ipca.transform(X)
          assert_almost_equal(
              np.abs(Xt_pca), np.abs(Xt_ipca), decimal=prec
          )
          Xinv_ipca = ipca.inverse_transform(Xt_ipca)
          Xinv_pca = pca.inverse_transform(Xt_pca)
          assert_almost_equal(X.compute(), Xinv_ipca, decimal=prec)
          assert_almost_equal(X.compute(), Xinv_pca, decimal=prec)
          assert_almost_equal(Xinv_pca, Xinv_ipca, decimal=prec)
  
imports: import numpy
  import pytest
  from dask import array
  from sklearn import datasets, decomposition
  from sklearn.decomposition import PCA
  from dask_ml.decomposition import IncrementalPCA
  from dask_ml.utils import flip_vector_signs
label_desc: Use image node_id 17 for calling a global function with example usage: test_whitening(svd_solver) without return types