code: def test_pca_explained_variance_equivalence_solver(svd_solver):
      rng = np.random.RandomState(0)
      n_samples, n_features = 100, 80
      X = rng.randn(n_samples, n_features)
  
      pca_full = PCA(n_components=2, svd_solver="full")
      pca_other = PCA(
          n_components=2, svd_solver=svd_solver, random_state=0
      )
  
      pca_full.fit(X)
      pca_other.fit(X)
  
      assert_allclose(
          pca_full.explained_variance_,
          pca_other.explained_variance_,
          rtol=5e-2,
      )
      assert_allclose(
          pca_full.explained_variance_ratio_,
          pca_other.explained_variance_ratio_,
          rtol=5e-2,
      )
  
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 8 for calling a global function with example usage: test_pca_explained_variance_equivalence_solver(svd_solver) without return types