code: def test_mixed_mok_with_Id_vs_independent_mok() -> None:
      data = DataMixedKernelWithEye
      # Independent model
      k1 = mk.SharedIndependent(
          SquaredExponential(variance=0.5, lengthscales=1.2), data.L
      )
      f1 = InducingPoints(data.X[: data.M, ...])
      model_1 = SVGP(
          k1,
          Gaussian(),
          f1,
          q_mu=data.mu_data_full,
          q_sqrt=data.sqrt_data_full,
      )
      set_trainable(model_1, False)
      set_trainable(model_1.q_sqrt, True)
  
      gpflow.optimizers.Scipy().minimize(
          model_1.training_loss_closure(Data.data),
          variables=model_1.trainable_variables,
          method="BFGS",
          compile=True,
      )
  
      # Mixed Model
      kern_list = [
          SquaredExponential(variance=0.5, lengthscales=1.2)
          for _ in range(data.L)
      ]
      k2 = mk.LinearCoregionalization(kern_list, data.W)
      f2 = InducingPoints(data.X[: data.M, ...])
      model_2 = SVGP(
          k2,
          Gaussian(),
          f2,
          q_mu=data.mu_data_full,
          q_sqrt=data.sqrt_data_full,
      )
      set_trainable(model_2, False)
      set_trainable(model_2.q_sqrt, True)
  
      gpflow.optimizers.Scipy().minimize(
          model_2.training_loss_closure(Data.data),
          variables=model_2.trainable_variables,
          method="BFGS",
          compile=True,
      )
  
      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 18 for calling a global function with example usage: test_mixed_mok_with_Id_vs_independent_mok() without return types