code: def create_conditional(
      *,
      kernel: Kernel,
      inducing_variable: InducingVariables,
      q_mu: TensorType,
      q_sqrt: TensorType,
      whiten: bool,
  ) -> ConditionalClosure:
      @check_shapes(
          "Xnew: [batch..., N, D]",
          "return[0]: [batch..., N, R]",
          "return[1]: [batch..., N, R] if (not full_cov) and (not full_output_cov)",
          "return[1]: [batch..., R, N, N] if full_cov and (not full_output_cov)",
          "return[1]: [batch..., N, R, R] if (not full_cov) and full_output_cov",
          "return[1]: [batch..., N, R, N, R] if full_cov and full_output_cov",
      )
      def conditional_closure(
          Xnew: TensorType, *, full_cov: bool, full_output_cov: bool
      ) -> tf.Tensor:
          return conditional(
              Xnew,
              inducing_variable,
              kernel,
              q_mu,
              q_sqrt=q_sqrt,
              white=whiten,
              full_cov=full_cov,
              full_output_cov=full_output_cov,
          )
  
      return conditional_closure
  
imports: from typing import Any, Callable, DefaultDict, Optional, Set, Type, cast
  import numpy
  import pytest
  import tensorflow
  from _pytest.fixtures import SubRequest
  from check_shapes import check_shapes
  import gpflow
  import gpflow.ci_utils
  from gpflow.base import TensorType
  from gpflow.conditionals import conditional
  from gpflow.inducing_variables import InducingPoints, InducingVariables
  from gpflow.kernels import Kernel
  from gpflow.likelihoods import Gaussian
  from gpflow.mean_functions import Zero
  from gpflow.models.util import inducingpoint_wrapper
  from gpflow.posteriors import AbstractPosterior, FallbackIndependentLatentPosterior, FullyCorrelatedPosterior, GPRPosterior, IndependentPosteriorMultiOutput, IndependentPosteriorSingleOutput, LinearCoregionalizationPosterior, PrecomputeCacheType, SGPRPosterior, VGPPosterior, create_posterior
label_desc: Use image node_id 6 for calling a global function with example usage: create_conditional() and returns: conditional_closure, conditional