code: def test_incremental_pca_check_projection():
      # Test that the projection of data is correct.
      rng = np.random.RandomState(1999)
      n, p = 100, 3
      X = rng.randn(n, p) * 0.1
      X[:10] += np.array([3, 4, 5])
      Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5])
  
      # Get the reconstruction of the generated data X
      # Note that Xt has the same "components" as X, just separated
      # This is what we want to ensure is recreated correctly
      Yt = IncrementalPCA(n_components=2).fit(X).transform(Xt)
  
      # Normalize
      Yt /= np.sqrt((Yt**2).sum())
  
      # Make sure that the first element of Yt is ~1, this means
      # the reconstruction worked as expected
      assert_almost_equal(np.abs(Yt[0][0]), 1.0, 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 3 for calling a global function with example usage: test_incremental_pca_check_projection() without return types