code: def test_pca_explained_variance_empirical(X, svd_solver):
      pca = PCA(n_components=2, svd_solver=svd_solver, random_state=0)
      X_pca = pca.fit_transform(X)
      assert_allclose(
          pca.explained_variance_, np.var(X_pca, ddof=1, axis=0)
      )
  
      expected_result = np.linalg.eig(np.cov(X, rowvar=False))[0]
      expected_result = sorted(expected_result, reverse=True)[:2]
      assert_allclose(
          pca.explained_variance_, expected_result, rtol=5e-3
      )
  
imports: import re
  import warnings
  import numpy
  import pytest
  import scipy
  from numpy.testing import assert_array_equal
  from sklearn import config_context, datasets
  from sklearn.base import clone
  from sklearn.datasets import load_iris, make_classification
  from sklearn.decomposition import PCA
  from sklearn.decomposition._pca import _assess_dimension, _infer_dimension
  from sklearn.utils._array_api import _atol_for_type, _convert_to_numpy, yield_namespace_device_dtype_combinations
  from sklearn.utils._array_api import device
  from sklearn.utils._testing import _array_api_for_tests, assert_allclose
  from sklearn.utils.estimator_checks import _get_check_estimator_ids, check_array_api_input_and_values
  from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
label_desc: Use image node_id 9 for calling a global function with example usage: test_pca_explained_variance_empirical(X, svd_solver) without return types