class_docstr: A custom kernel that has a diag method that returns the first column of the
  input matrix X. This is a helper for the test to check that the input
  matrix X is not mutated.
class_docstr_tok: ["A","custom","kernel","that","has","a","diag","method","that","returns","the","first","column","of","the","input","matrix","X",".","This","is","a","helper","for","the","test","to","check","that","the","input","matrix","X","is","not","mutated","."]
code: def diag(self, X):
      return X[:, 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 1 for calling the CustomKernel obj's underlying member method code with example usage: obj.diag(X) and returns: X