code: def _conditional_with_precompute(
      self, cache, Xnew, full_cov, full_output_cov
  ):
      alpha, Qinv = cache
  
      if full_output_cov:
          raise NotImplementedError
  
      Kuf = cov.Kuf(self.X_data, self.kernel, Xnew)  # still a Tensor
  
      # construct the conditional mean
      fmean = tf.matmul(Kuf, alpha, transpose_a=True)
  
      num_func = tf.shape(alpha)[1]  # K
      Qinv_Kuf = tf.matmul(Qinv, Kuf)
  
      # compute the covariance due to the conditioning
      if full_cov:
          fvar = self.kernel(Xnew) - tf.matmul(
              Kuf, Qinv_Kuf, transpose_a=True
          )
      else:
          KufT_Qinv_Kuf_diag = tf.reduce_sum(Kuf * Qinv_Kuf, axis=-2)
          fvar = self.kernel(Xnew, full_cov=False) - KufT_Qinv_Kuf_diag
          fvar = tf.transpose(fvar)
  
      return fmean, fvar
  
imports: import numpy
  import tensorflow
  from check_shapes import Shape
  import gpflow
  from gpflow import covariances
  from gpflow import kullback_leiblers
  from gpflow.base import TensorLike
  from gpflow.ci_utils import reduce_in_tests
  from gpflow.inducing_variables import InducingVariables
  from gpflow.utilities import to_default_float
  import matplotlib.pyplot
  import gpflow.posteriors
label_desc: Use image node_id 3 for calling the VFFPosterior obj's underlying member method code with example usage: obj._conditional_with_precompute(cache, Xnew, full_cov, full_output_cov) and returns: fmean,  fvar