code: def _precompute(self):
      Kuu = cov.Kuu(
          self.X_data, self.kernel
      )  # this is now a LinearOperator
  
      q_mu = self._q_dist.q_mu
      q_sqrt = self._q_dist.q_sqrt
  
      if self.whiten:
          raise NotImplementedError
      else:
          # alpha = Kuu⁻¹ q_mu
          alpha = Kuu.solve(q_mu)  # type: tf.Tensor
  
      if self.whiten:
          raise NotImplementedError
      else:
          # Qinv = Kuu⁻¹ - Kuu⁻¹ S Kuu⁻¹
          KuuInv_qsqrt = Kuu.solve(q_sqrt)
          KuuInv_covu_KuuInv = tf.matmul(
              KuuInv_qsqrt, KuuInv_qsqrt, transpose_b=True
          )
  
      Qinv = Kuu.inverse().to_dense() - KuuInv_covu_KuuInv
  
      return gpflow.posteriors.PrecomputedValue.wrap_alpha_Qinv(
          alpha, Qinv
      )
  
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 2 for calling the VFFPosterior obj's underlying member method code with example usage: obj._precompute() and returns: gpflow