code: def test_compare_mixed_kernel() -> None:
      data = DataMixedKernel
  
      kern_list = [SquaredExponential() for _ in range(data.L)]
      k1 = mk.LinearCoregionalization(kern_list, W=data.W)
      f1 = mf.SharedIndependentInducingVariables(
          InducingPoints(data.X[: data.M, ...])
      )
      model_1 = SVGP(
          k1,
          Gaussian(),
          inducing_variable=f1,
          q_mu=data.mu_data,
          q_sqrt=data.sqrt_data,
      )
  
      kern_list = [SquaredExponential() for _ in range(data.L)]
      k2 = mk.LinearCoregionalization(kern_list, W=data.W)
      f2 = mf.SharedIndependentInducingVariables(
          InducingPoints(data.X[: data.M, ...])
      )
      model_2 = SVGP(
          k2,
          Gaussian(),
          inducing_variable=f2,
          q_mu=data.mu_data,
          q_sqrt=data.sqrt_data,
      )
  
      check_equality_predictions(Data.data, [model_1, model_2])
  
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 19 for calling a global function with example usage: test_compare_mixed_kernel() without return types