code: def test_pca_check_projection():
      # Test that the projection of data is correct
      rng = np.random.RandomState(0)
      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])
      dX = da.from_array(X, chunks=(n, p))
      dXt = da.from_array(Xt, chunks=(n, p))
  
      for solver in solver_list:
          Yt = (
              dd.PCA(n_components=2, svd_solver=solver)
              .fit(dX)
              .transform(dXt)
          )
          Yt /= np.sqrt((Yt**2).sum())
  
          assert_almost_equal(np.abs(Yt[0][0]), 1.0, 1)
  
imports: from itertools import product
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import scipy
  import sklearn.decomposition
  from dask.array.utils import assert_eq
  from numpy.testing import assert_almost_equal, assert_array_almost_equal, assert_raises
  from sklearn import datasets
  from sklearn.utils import check_random_state
  import dask_ml.decomposition
  from dask_ml._compat import DASK_2_26_0
  from dask_ml.utils import assert_estimator_equal, flip_vector_signs, svd_flip
label_desc: Use image node_id 9 for calling a global function with example usage: test_pca_check_projection() without return types