code: def test_incremental_pca_against_pca_random_data(svd_solver):
      # Test that IncrementalPCA and PCA are approximate (to a sign flip).
      rng = np.random.RandomState(1999)
      n_samples = 100
      n_features = 3
      X = rng.randn(n_samples, n_features) + 5 * rng.rand(1, n_features)
      X = da.from_array(X, chunks=[40, -1])
  
      Y_pca = PCA(n_components=3, svd_solver=svd_solver).fit_transform(
          X.compute()
      )
      Y_ipca = IncrementalPCA(
          n_components=3, batch_size=25, svd_solver=svd_solver
      ).fit_transform(X)
  
      assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1)
  
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 14 for calling a global function with example usage: test_incremental_pca_against_pca_random_data(svd_solver) without return types