code: def test_precomputed():
      rs = np.random.RandomState(23)
      X = rs.normal(size=(10, 10))
      y = rs.normal(size=10)
      K = pairwise_kernels(X)
      precomputed_model = cuKernelRidge(kernel="precomputed")
      precomputed_model.fit(K, y)
      model = cuKernelRidge()
      model.fit(X, y)
      assert np.allclose(precomputed_model.dual_coef_, model.dual_coef_)
      assert np.allclose(
          precomputed_model.predict(K),
          model.predict(X),
          atol=1e-5,
          rtol=1e-5,
      )
  
imports: from cuml.testing.utils import as_type
  from hypothesis.extra.numpy import arrays
  from hypothesis import given, settings, assume, strategies
  from sklearn.kernel_ridge import KernelRidge
  import inspect
  import math
  import pytest
  from sklearn.metrics.pairwise import pairwise_kernels
  from cuml.metrics import pairwise_kernels, PAIRWISE_KERNEL_FUNCTIONS
  from cuml import KernelRidge
  from cuml.internals.safe_imports import cpu_only_import
  from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 9 for calling a global function with example usage: test_precomputed() without return types