code: def create_svgp__fallback_independent_latent_posterior(
      data: RegressionData,
  ) -> SVGP:
      n_outputs = data[1].shape[1]
      rng = np.random.default_rng(20220131)
      kernel = gpflow.kernels.LinearCoregionalization(
          [create_kernel()],
          W=tf.constant(rng.standard_normal((n_outputs, 1))),
      )
      inducing_variable = gpflow.inducing_variables.FallbackSeparateIndependentInducingVariables(
          [create_inducing_points(data)]
      )
      q_diag, q_mu, q_sqrt = create_q(inducing_variable)
      return SVGP(
          kernel=kernel,
          likelihood=create_likelihood(),
          inducing_variable=inducing_variable,
          q_diag=q_diag,
          q_mu=q_mu,
          q_sqrt=q_sqrt,
      )
  
imports: from typing import Any, Callable, DefaultDict, Generic, Iterator, List, Set, Tuple, Type, TypeVar, cast
  import numpy
  import pytest
  import tensorflow
  from _pytest.fixtures import SubRequest
  from check_shapes import check_shapes
  import gpflow
  from gpflow.base import RegressionData
  from gpflow.config import default_float
  from gpflow.inducing_variables import InducingPoints, InducingVariables
  from gpflow.kernels import Kernel, Matern52
  from gpflow.likelihoods import Exponential, Likelihood
  from gpflow.models import GPR, SGPR, SVGP, VGP, GPModel, training_loss_closure
  from gpflow.models.vgp import update_vgp_data
  from gpflow.posteriors import AbstractPosterior, PrecomputeCacheType
label_desc: Use image node_id 13 for calling a global function with example usage: create_svgp__fallback_independent_latent_posterior(data) and returns: SVGP