code: def test_separate_independent_mok() -> None:
      """
      We use different independent kernels for each of the output dimensions.
      We can achieve this in two ways:
          1) efficient: SeparateIndependentMok with Shared/SeparateIndependentMof
          2) inefficient: SeparateIndependentMok with InducingPoints
      However, both methods should return the same conditional,
      and after optimization return the same log likelihood.
      """
      # Model 1 (Inefficient)
      q_mu_1 = np.random.randn(Data.M * Data.P, 1)
      q_sqrt_1 = np.tril(
          np.random.randn(Data.M * Data.P, Data.M * Data.P)
      )[
          None, ...
      ]  # 1 x MP x MP
  
      kern_list_1 = [
          SquaredExponential(variance=0.5, lengthscales=1.2)
          for _ in range(Data.P)
      ]
      kernel_1 = mk.SeparateIndependent(kern_list_1)
      inducing_variable_1 = InducingPoints(Data.X[: Data.M, ...])
      model_1 = SVGP(
          kernel_1,
          Gaussian(),
          inducing_variable_1,
          num_latent_gps=1,
          q_mu=q_mu_1,
          q_sqrt=q_sqrt_1,
      )
      set_trainable(model_1, False)
      set_trainable(model_1.q_sqrt, True)
      set_trainable(model_1.q_mu, True)
  
      gpflow.optimizers.Scipy().minimize(
          model_1.training_loss_closure(Data.data),
          variables=model_1.trainable_variables,
          method="BFGS",
          compile=True,
      )
  
      # Model 2 (efficient)
      q_mu_2 = np.random.randn(Data.M, Data.P)
      q_sqrt_2: AnyNDArray = np.array(
          [
              np.tril(np.random.randn(Data.M, Data.M))
              for _ in range(Data.P)
          ]
      )  # P x M x M
      kern_list_2 = [
          SquaredExponential(variance=0.5, lengthscales=1.2)
          for _ in range(Data.P)
      ]
      kernel_2 = mk.SeparateIndependent(kern_list_2)
      inducing_variable_2 = mf.SharedIndependentInducingVariables(
          InducingPoints(Data.X[: Data.M, ...])
      )
      model_2 = SVGP(
          kernel_2,
          Gaussian(),
          inducing_variable_2,
          num_latent_gps=Data.P,
          q_mu=q_mu_2,
          q_sqrt=q_sqrt_2,
      )
      set_trainable(model_2, False)
      set_trainable(model_2.q_sqrt, True)
      set_trainable(model_2.q_mu, 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 16 for calling a global function with example usage: test_separate_independent_mok() without return types