code: def test_pca_score():
      # Test that probabilistic PCA scoring yields a reasonable score
      n, p = 1000, 3
      rng = np.random.RandomState(0)
      X = rng.randn(n, p) * 0.1 + np.array([3, 4, 5])
      dX = da.from_array(X, chunks=(n // 2, p))
      for solver in solver_list:
          pca = dd.PCA(n_components=2, svd_solver=solver)
          pca.fit(dX)
          ll1 = pca.score(dX)
          h = -0.5 * np.log(2 * np.pi * np.exp(1) * 0.1**2) * p
          np.testing.assert_almost_equal(ll1 / h, 1, 0)
  
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 21 for calling a global function with example usage: test_pca_score() without return types