code: def test_eigen_vs_svd():
      pca = PCA(n_components=2, solver="eigen")
      eigen_res = pca.fit(X_std).transform(X_std)
      pca = PCA(n_components=2, solver="svd")
      svd_res = pca.fit(X_std).transform(X_std)
      assert_allclose(
          np.absolute(eigen_res), np.absolute(svd_res), atol=0.0001
      )
  
file_path: mlxtend/mlxtend/feature_extraction/tests/test_principal_component_analysis.py
imports: import numpy
  from numpy.testing import assert_allclose, assert_almost_equal
  from mlxtend.data import iris_data
  from mlxtend.feature_extraction import PrincipalComponentAnalysis
  from mlxtend.preprocessing import standardize
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 4 for calling a global function with example usage: test_eigen_vs_svd() without return types