code: def test_polynomial_count_sketch(gamma, degree, coef0, n_components):
      # test that PolynomialCountSketch approximates polynomial
      # kernel on random data
  
      # compute exact kernel
      kernel = polynomial_kernel(
          X, Y, gamma=gamma, degree=degree, coef0=coef0
      )
  
      # approximate kernel mapping
      ps_transform = PolynomialCountSketch(
          n_components=n_components,
          gamma=gamma,
          coef0=coef0,
          degree=degree,
          random_state=42,
      )
      X_trans = ps_transform.fit_transform(X)
      Y_trans = ps_transform.transform(Y)
      kernel_approx = np.dot(X_trans, Y_trans.T)
  
      error = kernel - kernel_approx
      assert np.abs(np.mean(error)) <= 0.05  # close to unbiased
      np.abs(error, out=error)
      assert np.max(error) <= 0.1  # nothing too far off
      assert np.mean(error) <= 0.05
  
imports: import re
  import numpy
  import pytest
  from sklearn.datasets import make_classification
  from sklearn.kernel_approximation import AdditiveChi2Sampler, Nystroem, PolynomialCountSketch, RBFSampler, SkewedChi2Sampler
  from sklearn.metrics.pairwise import chi2_kernel, kernel_metrics, polynomial_kernel, rbf_kernel
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 1 for calling a global function with example usage: test_polynomial_count_sketch(gamma, degree, coef0, n_components) without return types