class_docstr: This is the Sparse Variational GP (SVGP).
  
  The key reference is :cite:t:`hensman2014scalable`.
  
  This class provides a posterior() method that enables caching for faster subsequent predictions.
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docstr: For backwards compatibility, SVGP's predict_f uses the fused (no-cache)
  computation, which is more efficient during training.
  
  For faster (cached) prediction, predict directly from the posterior object, i.e.,:
      model.posterior().predict_f(Xnew, ...)
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code: def predict_f(
      self,
      Xnew: InputData,
      full_cov: bool = False,
      full_output_cov: bool = False,
  ) -> MeanAndVariance:
      """
      For backwards compatibility, SVGP's predict_f uses the fused (no-cache)
      computation, which is more efficient during training.
  
      For faster (cached) prediction, predict directly from the posterior object, i.e.,:
          model.posterior().predict_f(Xnew, ...)
      """
      return self.posterior(
          posteriors.PrecomputeCacheType.NOCACHE
      ).fused_predict_f(
          Xnew, full_cov=full_cov, full_output_cov=full_output_cov
      )
  
imports: from typing import Optional
  import numpy
  import tensorflow
  from check_shapes import check_shapes, inherit_check_shapes
  from ..None import kullback_leiblers, posteriors
  from ..base import AnyNDArray, InputData, MeanAndVariance, Parameter, RegressionData
  from ..conditionals import conditional
  from ..config import default_float
  from ..inducing_variables import InducingVariables
  from ..kernels import Kernel
  from ..likelihoods import Likelihood
  from ..mean_functions import MeanFunction
  from ..utilities import positive, triangular
  from .model import GPModel
  from .training_mixins import ExternalDataTrainingLossMixin
  from .util import InducingVariablesLike, inducingpoint_wrapper
label_desc: Use image node_id 2 for calling the SVGP_with_posterior obj's underlying member method code with example usage: obj.predict_f(Xnew, full_cov, full_output_cov) and returns: self