code: def test_singular_values_expected(solver):
      # Set the singular values and see what we get back
      rng = np.random.RandomState(0)
      n_samples = 100
      n_features = 110
  
      X = rng.randn(n_samples, n_features)
  
      pca = TruncatedSVD(
          n_components=3, algorithm=solver, random_state=rng
      )
      X_pca = pca.fit_transform(X)
  
      X_pca /= np.sqrt(np.sum(X_pca**2.0, axis=0))
      X_pca[:, 0] *= 3.142
      X_pca[:, 1] *= 2.718
  
      X_hat_pca = np.dot(X_pca, pca.components_)
      pca.fit(X_hat_pca)
      assert_allclose(
          pca.singular_values_, [3.142, 2.718, 1.0], rtol=1e-14
      )
  
file_path: scikit-learn/sklearn/decomposition/tests/test_truncated_svd.py
imports: import numpy
  import pytest
  import scipy.sparse
  from sklearn.decomposition import PCA, TruncatedSVD
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_allclose, assert_array_less
label_desc: Use image node_id 11 for calling a global function with example usage: test_singular_values_expected(solver) without return types