class_docstr: This method approximates the Gaussian process posterior using a multivariate Gaussian.
  
  The idea is that the posterior over the function-value vector F is
  approximated by a Gaussian, and the KL divergence is minimised between
  the approximation and the posterior.
  
  This implementation is equivalent to SVGP with X=Z, but is more efficient.
  The whitened representation is used to aid optimization.
  
  The posterior approximation is
  
  .. math::
  
     q(\mathbf f) = N(\mathbf f \,|\, \boldsymbol \mu, \boldsymbol \Sigma)
  
  For a use example see
  :doc:`../../../../notebooks/getting_started/classification_and_other_data_distributions`.
code: def predict_f(
      self,
      Xnew: InputData,
      full_cov: bool = False,
      full_output_cov: bool = False,
  ) -> MeanAndVariance:
      assert_params_false(
          self.predict_f, full_output_cov=full_output_cov
      )
  
      X_data, _Y_data = self.data
      mu, var = conditional(
          Xnew,
          X_data,
          self.kernel,
          self.q_mu,
          q_sqrt=self.q_sqrt,
          full_cov=full_cov,
          white=True,
      )
      return mu + self.mean_function(Xnew), var
  
imports: from typing import Optional
  import numpy
  import tensorflow
  from check_shapes import check_shapes, inherit_check_shapes
  import gpflow
  from ..None import posteriors
  from ..base import InputData, MeanAndVariance, Parameter, RegressionData
  from ..conditionals import conditional
  from ..config import default_float, default_jitter
  from ..kernels import Kernel
  from ..kullback_leiblers import gauss_kl
  from ..likelihoods import Likelihood
  from ..mean_functions import MeanFunction
  from ..utilities import assert_params_false, is_variable, triangular, triangular_size
  from .model import GPModel
  from .training_mixins import InternalDataTrainingLossMixin
  from .util import data_input_to_tensor
label_desc: Use image node_id 4 for calling the VGP_deprecated obj's underlying member method code with example usage: obj.predict_f(Xnew, full_cov, full_output_cov) and returns: unknown,  var