code: def test_pca_score_with_different_solvers():
      digits = datasets.load_digits()
      X_digits = digits.data
  
      dX_digits = da.from_array(X_digits, chunks=X_digits.shape)
  
      pca_dict = {
          svd_solver: dd.PCA(
              n_components=30,
              svd_solver=svd_solver,
              random_state=0,
              iterated_power=3,
          )
          for svd_solver in solver_list
      }
  
      for pca in pca_dict.values():
          pca.fit(dX_digits)
          # Sanity check for the noise_variance_. For more details see
          # https://github.com/scikit-learn/scikit-learn/issues/7568
          # https://github.com/scikit-learn/scikit-learn/issues/8541
          # https://github.com/scikit-learn/scikit-learn/issues/8544
          assert np.all(
              (pca.explained_variance_ - pca.noise_variance_) >= 0
          )
  
      # Compare scores with different svd_solvers
      score_dict = {
          svd_solver: pca.score(dX_digits)
          for svd_solver, pca in pca_dict.items()
      }
      assert_almost_equal(
          score_dict["full"], score_dict["randomized"], decimal=3
      )
  
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 24 for calling a global function with example usage: test_pca_score_with_different_solvers() without return types