code: def test_incremental_pca_against_pca_iris():
      # Test that IncrementalPCA and PCA are approximate (to a sign flip).
      X = iris.data
  
      Y_pca = PCA(n_components=2).fit_transform(X)
      Y_ipca = IncrementalPCA(
          n_components=2, batch_size=25
      ).fit_transform(X)
  
      assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1)
  
imports: import warnings
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from sklearn import datasets
  from sklearn.decomposition import PCA, IncrementalPCA
  from sklearn.utils._testing import assert_allclose_dense_sparse, assert_almost_equal, assert_array_almost_equal
  from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS, LIL_CONTAINERS
label_desc: Use image node_id 14 for calling a global function with example usage: test_incremental_pca_against_pca_iris() without return types