code: def test_randomized_pca_check_list():
      # Test that the projection by randomized PCA on list data is correct
      X = [[1.0, 0.0], [0.0, 1.0]]
      X_transformed = (
          dd.PCA(
              n_components=1, svd_solver="randomized", random_state=0
          )
          .fit(X)
          .transform(X)
      )
      assert X_transformed.shape == (2, 1)
      assert_almost_equal(X_transformed.mean(), 0.00, 2)
      assert_almost_equal(X_transformed.std(), 0.71, 2)
  
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 14 for calling a global function with example usage: test_randomized_pca_check_list() without return types