code: def test_randomized_pca_check_list(setup):
      # Test that the projection by randomized PCA on list data is correct
      X = mt.tensor([[1.0, 0.0], [0.0, 1.0]])
      X_transformed = (
          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().to_numpy(), 0.00, 2)
      assert_almost_equal(X_transformed.std().to_numpy(), 0.71, 2)
  
imports: from itertools import product
  import numpy
  import pytest
  import scipy
  from sklearn import datasets
  from sklearn.utils._testing import assert_array_almost_equal, assert_almost_equal, assert_raises_regex, assert_raise_message, assert_raises
  from ....None import tensor
  from .._pca import PCA, _assess_dimension, _infer_dimension
label_desc: Use image node_id 11 for calling a global function with example usage: test_randomized_pca_check_list(setup) without return types