code: def test_kernel_pca_pandas_output():
      """Check that KernelPCA works with pandas output when the solver is arpack.
  
      Non-regression test for:
      https://github.com/scikit-learn/scikit-learn/issues/27579
      """
      pytest.importorskip("pandas")
      X, _ = load_iris(as_frame=True, return_X_y=True)
      with sklearn.config_context(transform_output="pandas"):
          KernelPCA(
              n_components=2, eigen_solver="arpack"
          ).fit_transform(X)
  
imports: import warnings
  import numpy
  import pytest
  import sklearn
  from sklearn.datasets import load_iris, make_blobs, make_circles
  from sklearn.decomposition import PCA, KernelPCA
  from sklearn.exceptions import NotFittedError
  from sklearn.linear_model import Perceptron
  from sklearn.metrics.pairwise import rbf_kernel
  from sklearn.model_selection import GridSearchCV
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import CSR_CONTAINERS
  from sklearn.utils.validation import _check_psd_eigenvalues
label_desc: Use image node_id 23 for calling a global function with example usage: test_kernel_pca_pandas_output() without return types