code: def test_kernel_pca_invalid_parameters():
      """Check that kPCA raises an error if the parameters are invalid
  
      Tests fitting inverse transform with a precomputed kernel raises a
      ValueError.
      """
      estimator = KernelPCA(
          n_components=10,
          fit_inverse_transform=True,
          kernel="precomputed",
      )
      err_ms = "Cannot fit_inverse_transform with a precomputed kernel"
      with pytest.raises(ValueError, match=err_ms):
          estimator.fit(np.random.randn(10, 10))
  
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 2 for calling a global function with example usage: test_kernel_pca_invalid_parameters() without return types