code: def test_MixedMok_Kgg() -> None:
      data = DataMixedKernel
      kern_list = [SquaredExponential() for _ in range(data.L)]
      kernel = mk.LinearCoregionalization(kern_list, W=data.W)
  
      Kgg = kernel.Kgg(Data.X, Data.X)  # L x N x N
      Kff = kernel.K(Data.X, Data.X)  # N x P x N x P
  
      # Kff = W @ Kgg @ W^T
      Kff_infered = np.einsum("lnm,pl,ql->npmq", Kgg, data.W, data.W)
  
      np.testing.assert_array_almost_equal(Kff, Kff_infered, decimal=5)
  
imports: from typing import Callable, List, Sequence, Tuple, cast
  import numpy
  import pytest
  import scipy
  import tensorflow
  from _pytest.fixtures import SubRequest
  from check_shapes import ShapeChecker, check_shapes
  import gpflow
  import gpflow.inducing_variables.multioutput
  import gpflow.kernels.multioutput
  from gpflow import set_trainable
  from gpflow.base import AnyNDArray, RegressionData
  from gpflow.conditionals import sample_conditional
  from gpflow.conditionals.util import fully_correlated_conditional, fully_correlated_conditional_repeat, independent_interdomain_conditional, sample_mvn
  from gpflow.config import default_float, default_jitter
  from gpflow.inducing_variables import InducingPoints
  from gpflow.kernels import SquaredExponential
  from gpflow.likelihoods import Gaussian
  from gpflow.models import SVGP
label_desc: Use image node_id 14 for calling a global function with example usage: test_MixedMok_Kgg() without return types