code: def test_pca_sanity_noise_variance(svd_solver):
      # 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
      X, _ = datasets.load_digits(return_X_y=True)
      pca = PCA(n_components=30, svd_solver=svd_solver, random_state=0)
      pca.fit(X)
      assert np.all(
          (pca.explained_variance_ - pca.noise_variance_) >= 0
      )
  
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 26 for calling a global function with example usage: test_pca_sanity_noise_variance(svd_solver) without return types