code: def test_gpr_interpolation_structured():
      # Test the interpolating property for different kernels.
      kernel = MiniSeqKernel(baseline_similarity_bounds="fixed")
      X = ["A", "B", "C"]
      y = np.array([1, 2, 3])
      gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
      y_pred, y_cov = gpr.predict(X, return_cov=True)
  
      assert_almost_equal(
          kernel(X, eval_gradient=True)[1].ravel(),
          (1 - np.eye(len(X))).ravel(),
      )
      assert_almost_equal(y_pred, y)
      assert_almost_equal(np.diag(y_cov), 0.0)
  
imports: import re
  import sys
  import warnings
  import numpy
  import pytest
  from scipy.optimize import approx_fprime
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.gaussian_process import GaussianProcessRegressor
  from sklearn.gaussian_process.kernels import RBF, DotProduct, ExpSineSquared, WhiteKernel
  from sklearn.gaussian_process.kernels import ConstantKernel
  from sklearn.gaussian_process.tests._mini_sequence_kernel import MiniSeqKernel
  from sklearn.utils._testing import assert_allclose, assert_almost_equal, assert_array_almost_equal, assert_array_less
label_desc: Use image node_id 3 for calling a global function with example usage: test_gpr_interpolation_structured() without return types